In previous studies, several methods for segmentation of the region of interest and joint classification have been proposed. For instance, Yang et al. [1]} created a multi-task deep neural network for skin lesion analysis to solve different tasks simultaneously such as lesion segmentation and two independent binary lesion classifications. The MTL model improved learning efficiency and prediction accuracy for each task, in comparison to single task models. They achieved an average Jaccard score of 0.724 for lesion segmentation, while the average values of the area under the receiver operating characteristic curve (AUC) on two lesion classifications are 0.880 and 0.972, respectively. The model consists of a common encoder for the 3 tasks based on GoogleNet [2]}, one decoder for segmentation and two fully connected branches for classification. In [3]} Asgari et al. proposed a multi-class segmentation as multi-task learning for drusen segmentation in retinal optical coherence tomography. The model is based on a multi-decoder architecture that tackles drusen segmentation as a multi-task problem. Instead of training a multi-class model for two classes segmentation, they used one decoder per target class and an extra task for the area between the layers. The model was validated on a dataset of 366 images. They achieved a mean dice of 0.73 compared to 0.68 with multi-class U-Net or 0.66 with a binary U-Net.
